Indiziert in
  • Öffnen Sie das J-Tor
  • Genamics JournalSeek
  • Akademische Schlüssel
  • JournalTOCs
  • Forschungsbibel
  • Ulrichs Zeitschriftenverzeichnis
  • Zugang zu globaler Online-Forschung in der Landwirtschaft (AGORA)
  • Elektronische Zeitschriftenbibliothek
  • RefSeek
  • Hamdard-Universität
  • EBSCO AZ
  • OCLC – WorldCat
  • SWB Online-Katalog
  • Virtuelle Bibliothek für Biologie (vifabio)
  • Publons
  • MIAR
  • Genfer Stiftung für medizinische Ausbildung und Forschung
  • Euro-Pub
  • Google Scholar
Teile diese Seite
Zeitschriftenflyer
Flyer image

Abstrakt

Antigenic Structural Similarity as a Predictor for Antibody Cross-Reactivity

Christopher A. Beaudoin*, Tom L. Blundell

Antibodies are an essential component of the adaptive immune system that function to neutralize foreign invaders, such as bacterial and parasitic pathogens. However, B-cell epitopes remain difficult to predict due to their general indistinguishability from other protein regions. Epitope prediction tools in the past have largely relied on amino acid sequence similarity; however, implementing three-dimensional protein structure analyses into the epitope prediction algorithms has been shown to increase detection accuracy. Furthermore, structural comparisons between antigenic proteins for their potential to bind cross-reactive antibodies have not been explored extensively in the literature. Recent studies have pointed to the utility of looking at shared epitope structures in predicting antibody crossreactivity, which may shed light on cross-immunity between infectious pathogens and autoimmune diseases induced after infection. Thus, herein, the potential impact of including structural similarity comparisons in detecting shared epitopes is discussed. With the large amount of structural information being determined by three-dimensional computational protein modelling methods, the ability to perform these analyses is becoming more feasible.

Haftungsausschluss: Dieser Abstract wurde mit Hilfe von Künstlicher Intelligenz übersetzt und wurde noch nicht überprüft oder verifiziert.